Version 1
: Received: 28 June 2024 / Approved: 1 July 2024 / Online: 2 July 2024 (04:07:44 CEST)
How to cite:
Herrera, A. R.; Alvarez, J.; Restrepo, J.; Herrera, C.; Rodríguez, S.; Escobar, C. A.; Vásquez, R. E.; Sierra-Perez, J. Damage Detection and Localization Methodology Based on Strain Measurements and Finite Element Analysis: SHM in the Context of Industry 4.0. Preprints2024, 2024070110. https://doi.org/10.20944/preprints202407.0110.v1
Herrera, A. R.; Alvarez, J.; Restrepo, J.; Herrera, C.; Rodríguez, S.; Escobar, C. A.; Vásquez, R. E.; Sierra-Perez, J. Damage Detection and Localization Methodology Based on Strain Measurements and Finite Element Analysis: SHM in the Context of Industry 4.0. Preprints 2024, 2024070110. https://doi.org/10.20944/preprints202407.0110.v1
Herrera, A. R.; Alvarez, J.; Restrepo, J.; Herrera, C.; Rodríguez, S.; Escobar, C. A.; Vásquez, R. E.; Sierra-Perez, J. Damage Detection and Localization Methodology Based on Strain Measurements and Finite Element Analysis: SHM in the Context of Industry 4.0. Preprints2024, 2024070110. https://doi.org/10.20944/preprints202407.0110.v1
APA Style
Herrera, A. R., Alvarez, J., Restrepo, J., Herrera, C., Rodríguez, S., Escobar, C. A., Vásquez, R. E., & Sierra-Perez, J. (2024). Damage Detection and Localization Methodology Based on Strain Measurements and Finite Element Analysis: SHM in the Context of Industry 4.0. Preprints. https://doi.org/10.20944/preprints202407.0110.v1
Chicago/Turabian Style
Herrera, A. R., Rafael E. Vásquez and Julian Sierra-Perez. 2024 "Damage Detection and Localization Methodology Based on Strain Measurements and Finite Element Analysis: SHM in the Context of Industry 4.0" Preprints. https://doi.org/10.20944/preprints202407.0110.v1
Abstract
This paper investigates the integration of SHM within the frame of Industry 4.0 technologies, highlighting the potential for intelligent infrastructure management through the utilization of Big Data analytics, Machine Learning, and the Internet of Things (IoT). The study also presents a success case focused on a novel SHM methodology for detecting and locating damages in metallic aircraft structures, employing dimensional reduction techniques such as Principal Component Analysis (PCA). By analyzing strain data collected from a network of sensors and comparing it to a baseline pristine condition, the methodology aims to identify subtle changes in local strain distribution indicative of damage. Through extensive Finite Element Analysis (FEA) simulations and a PCA contribution analysis, the research explores the influence of various factors on damage detection, including sensor placement, noise levels, and damage size and type. The findings demonstrate the effectiveness of the proposed methodology in detecting cracks and holes as small as 2mm in length, showcasing the potential for early damage identification and targeted interventions in diverse sectors such as aerospace, civil engineering, and manufacturing. Ultimately, this paper underscores the synergistic relationship between SHM and Industry 4.0, paving the way for a future of intelligent, resilient, and sustainable infrastructure.
Keywords
Industry 4.0; Structural Health Monitoring; damage detection and localization; Machine learning; Artificial intelligence
Subject
Engineering, Aerospace Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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